def plot(masking: "Masking", mplot: plt) -> plt:
"""
Plot layer wise density bar plot.
:param masking: Masking instance
:type masking: sparselearning.core.Masking
:param mplot: matplotlib object
:type mplot: pyplot
:return: matplotlib plot
:rtype: pyplot
"""
density_ll = _get_density_ll(masking)
bin_ll = np.arange(len(density_ll)) + 1
width = 0.8
mplot.clf()
mplot.bar(bin_ll, density_ll, width, color="b")
# Gets too crowded when including layer names
# layer_name_ll = list(masking.masks.keys())
# plt.xticks(bin_ll, layer_name_ll)
mplot.ylabel("Density")
mplot.xlabel("Layer Number")
return mplot
def plot_choice(num: int, plot: plt, df1: pd.DataFrame) -> None:
"""
Function to re-draw the canvas
:param num: int of the RadioButton selection
:param plot: plt Matplotlib.pyplot instance
:param df1: pd.DataFrame of the TSA data
:return: None
"""
plot.clf()
blue = "#327ff6"
gray = "#bdb8b6"
if num == 1:
# Daily raw pax numbers
fig, axis = plt.subplots()
axis.plot(df1['Date'], df1['2020'], color=blue, linestyle='-', label="2020")
axis.plot(df1['Date'], df1['2019'], color=gray, linestyle='-', label="2019")
plot.legend()
plot.xlabel('Date')
plot.ylabel('Passengers')
plot.title('TSA Passenger Daily Throughput 2019 and 2020')
axis.get_yaxis().set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))
plot_new_canvas(fig, root)
elif num == 2:
# Weekly raw pax numbers
weekly_data = df1.resample('W-Mon', label='right', closed='right',
on='Date').sum().reset_index().sort_values(by='Date')
fig, axis = plt.subplots()
axis.plot(weekly_data['Date'], weekly_data['2020'], color=blue, linestyle='-',
label="2020")
axis.plot(weekly_data['Date'], weekly_data['2019'], color=gray, linestyle='-',
label="2019")
axis.get_yaxis().set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))
plot.xlabel('Date')
plot.ylabel('Passengers')
plot.legend()
plot.ylim(0, 20E6)
plot.title('TSA Passenger Weekly Throughput 2019 and 2020')
plot_new_canvas(fig, root)
elif num == 3:
# Daily YoY percentage
fig, axis = plt.subplots()
axis.plot(df1['Date'], df1['yoy'], color=blue, linestyle='-', label="2020")
plot.legend()
plot.xlabel('Date')
plot.ylabel('Passenger Load Factor (%)')
plot.title('TSA Passenger Daily Throughput in 2020 as a Percentage of 2019')
plot_new_canvas(fig, root)
elif num == 4:
# Weekly YoY percentage
weekly_data = df1.resample('W-Mon', label='right', closed='right',
on='Date').sum().reset_index().sort_values(by='Date')
weekly_data['yoy'] = (weekly_data['2020']/weekly_data['2019'])*100
fig, axis = plt.subplots()
axis.plot(weekly_data['Date'], weekly_data['yoy'], color=blue, linestyle='-',
label="2020")
plot.xlabel('Date')
plot.ylabel('Passenger Load Factor (%)')
plot.legend()
plot.title('TSA Passenger Daily Throughput in 2020 as a Percentage of 2019')
plot_new_canvas(fig, root)
